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<p>Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have achieved good results in general, impact detection within falls remains challenging. This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joint skeleton data treated as a graph using spatio-temporal graph convolutional networks (STGCNs), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM) layers. By pinpointing impact moments, our approach enhances precision by distinguishing between false falls and actual impacts, contributing to better healthcare resource allocation. Our methodology, evaluated using the improved 3D skeletons UP-Fall dataset, achieves accuracy exceeding 90% across various fall scenarios. We have made this improved dataset publicly available at <a href="https://zenodo.org/records/12773013">https://zenodo.org/records/12773013</a> to facilitate further research.</p>

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Impact Detection in Fall Events: Leveraging Spatio-temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeleton Data

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Tresor Y. Koffi,
  • Youssef Mourchid,
  • Mohammed Hindawi,
  • Yohan Dupuis

摘要

Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have achieved good results in general, impact detection within falls remains challenging. This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joint skeleton data treated as a graph using spatio-temporal graph convolutional networks (STGCNs), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM) layers. By pinpointing impact moments, our approach enhances precision by distinguishing between false falls and actual impacts, contributing to better healthcare resource allocation. Our methodology, evaluated using the improved 3D skeletons UP-Fall dataset, achieves accuracy exceeding 90% across various fall scenarios. We have made this improved dataset publicly available at https://zenodo.org/records/12773013 to facilitate further research.